Quantifying Effort in American Football

Emily Shteynberg, Luke Snavely, Sheryl Solorzano

Background and Motivation

  • Not all rushing yards are equal

  • Traditional stats miss the “how” behind yards gained

  • Previous research has explored athletes reaching theoretical max capacity 1

  • Can we “measure” effort using tracking data?

  • Multiple ways of evaluating effort

    • Intangible and subjective
    • Depends on player position, defense faced, game context, snap count/play volume, play call/assignment

Our Data: 2022 NFL Season

  • Game, play, player, tracking data from Weeks 1-9 1

  • Running plays where a running back (RB) is the ball carrier

  • Trimmed each play to frames between handoff and end of play

Motivation: acceleration-speed (AS) profiles tell us about players’ acceleration capacities at different speeds

Effort is defined as the percentage of points above the relaxed line

  • Biased toward backups because of lower sample size.. or are they just working harder after all? Are starters fatigued or pacing themselves?

  • Unrealistic theoretical max speeds

Plan of action

  • Define research question and scope ✅

  • Data cleaning and preprocessing, EDA ✅

  • Select effort metric(s)

  • Evaluate effort metric(s) by correlating it to effort-related outcomes

  • Use chosen metric(s) to answer questions related to effort (residual effort, tackle outcome probabilities, minimum required effort per play, etc)

Appendix